Linkage analyses are used to construct accurate genetic maps and to map disease genes. Mapping a disease gene can lead to many benefits for public health, including an understanding of the etiology of the disease, more accurate diagnoses by molecular or genetic means, prenatal diagnosis and genetic counseling, preventative measures (such as diet modification for those persons at risk due to inherited disease alleles), amelioration via gene therapy, and positional cloning of the disease gene. The main goal of the research is to develop novel algorithms that facilitate and accelerate the construction of more accurate genetic maps and localization of disease genes on these maps. As genetic marker maps have improved, multipoint linkage analysis has become a crucial part of all disease mapping studies. Paradoxically, it has also become increasingly difficult to compute multipoint lod scores, particularly as the numbers of markers, marker alleles, and untyped people increase. The investigators have made an advance in solving this problem by using a novel set-recoding scheme to recode each person's genotype and "fuzzy inheritance" to infer transmission probabilities; they have implemented their approach in a memory-efficient computer program, VITESSE, for extremely rapid computation of exact multipoint likelihoods. VITESSE enables fast and precise multipoint mapping of disease loci with highly polymorphic markers. However, there still remains much to do in terms of extending and developing VITESSE, as proposed here. The investigators plan to extend VITESSE to handle general pedigrees, peeling orders, and loops; to parallelize VITESSE and to develop techniques for rapid accurate approximation of multipoint likelihood curves; to extend VITESSE to handle genetic interference and multilocus disease models; and to integrate VITESSE with other programs. Linkage data, whether for generating marker maps or for mapping disease genes, are often gathered at great cost. Thus, it is crucial to be able to extract the maximum amount of information from the data, which can only be done by exact multipoint likelihood computations. It appears that researchers will always be pushing the computational limits, hoping for better computational tools for the best-possible analyses of their data. The investigators intend to provide these tools.